This article provides a complete, practical walkthrough of the Wait Splitout Send Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Expert setup in 2-4 hours. One‑time purchase: €149.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:
   Build a Smart WhatsApp Chatbot with AI Using n8n and Google Gemini
   
   Meta Description:
   Learn how to create a smart WhatsApp chatbot using n8n's workflow automation and AI capabilities. This tutorial walks you through analyzing and responding to text, image, audio, and video messages with the help of Google Gemini and LangChain agents.
   
   Keywords:
   n8n WhatsApp chatbot, Google Gemini API, LangChain AI agent, WhatsApp automation, AI chat assistant, WhatsApp bot builder, generative AI, multimodal AI, WhatsApp image transcription, WhatsApp audio transcription
   
   Third-Party APIs Used:
   
   1. WhatsApp Business API  
   2. Google Gemini (PaLM) API  
   3. LangChain (n8n LangChain integration)  
   4. Wikipedia (via LangChain toolWikipedia node)
   
   Article:
   
   Building an AI-Powered WhatsApp Chatbot with n8n and Google Gemini
   
   In a world where instant communication is key, automated chatbots are a popular tool for handling customer support, responding to FAQs, and even booking appointments—all within messaging apps like WhatsApp. Thanks to the powerful open-source workflow automation tool n8n and AI models like Google Gemini, building an intelligent, multimodal chatbot is more accessible than ever.
   
   In this tutorial, we'll break down a robust n8n workflow that transforms WhatsApp into an intelligent AI assistant—capable of interpreting not only text but also audio, images, and videos sent by users.
   
   🟢 Step 1: Triggering the Workflow with WhatsApp
   
   The journey begins with the WhatsApp Trigger node. This node listens for new messages from users, capturing various media types—text, images, video, and audio messages. With the help of a Split Out node, each incoming message is isolated and passed into a conditional Switch node.
   
   🧠 Step 2: Routing Based on Media Type
   
   The "Redirect Message Types" Switch node determines which path the message takes depending on its format:
   
   - Audio Messages
   - Video Messages
   - Image Messages
   - Text Messages (fallback/default option)
   
   Each branch routes the message to a specialized processing system powered by AI to interpret the content appropriately.
   
   🎧 Step 3: Transcribing Audio Messages with Google Gemini
   
   Audio messages are downloaded using the media URL from WhatsApp. These binary files are then sent to the Google Gemini 1.5 Pro model using an HTTP POST request. The response contains a transcription of the user's voice note, which is later fed into the AI Agent for context-aware replies.
   
   📽 Step 4: Describing Video Messages via Multimodal AI
   
   Using a similar approach as with audio, video files are fetched and sent to Google Gemini. Since n8n doesn’t natively support multimodal models yet, an HTTP node is used to craft a custom POST request with video content. Gemini responds with a textual description of the scene—helpful context for generating intelligent responses.
   
   🌅 Step 5: Explaining Images Using GPT-4o and LangChain
   
   Image messages are processed with the help of LangChain's chainLlm node using a prompt like: “Describe the image and transcribe any visible text.” The processing uses a GPT-like model under the hood (e.g., OpenAI's GPT-4o). The AI interprets the visual input, providing insights or relaying texts found in the image.
   
   ✉️ Step 6: Summarizing Text Messages
   
   For standard text messages, the workflow uses a text summarizer powered by LangChain. No further transformations are necessary, but the summarized content makes it easier for the AI Agent to respond accurately and concisely.
   
   🧠 Step 7: Feeding Context into an AI Agent
   
   Once the message content is appropriately transcribed, described, or summarized, a LangChain AI Agent takes over. The agent receives a structured prompt including:
   
   - Message type: text, image, audio, or video
   - Interpreted message content or description
   - Any accompanying captions
   
   This AI Agent—configured with memory and an optional Wikipedia tool—provides factual, concise responses that simulate a helpful assistant available via WhatsApp.
   
   📤 Step 8: Responding to the User via WhatsApp
   
   Finally, the generated response is sent back through the WhatsApp node. Depending on the use case, you could extend this to include rich media, documents, or even dynamic quick replies.
   
   🤖 Why This Workflow Matters
   
   By combining n8n, LangChain, and Google Gemini, this WhatsApp chatbot supports:
   
   - Voice note transcription
   - Visual analysis of images and videos
   - Multi-language support
   - Conversational memory context
   - Wikipedia-powered reasoning
   
   The workflow is perfect for businesses, educators, and developers looking to automate interaction on WhatsApp beyond simple FAQ bots.
   
   🔧 Requirements
   
   To make this workflow function end-to-end, you’ll need:
   
   - Access to the WhatsApp Business API
   - A Google Cloud account with Gemini API access
   - LangChain integration setup in n8n
   
   📌 Conclusion
   
   With this n8n workflow, you now have the building blocks for an AI-powered WhatsApp chatbot that handles multimedia and text seamlessly. Best of all, it's fully customizable—whether you're automating customer service or building an educational assistant.
   
   So why wait? Give your users the smart assistant they deserve—right inside the world’s most popular messaging app.
   
   —
   
   👥 Join the Community
   
   Need help adapting this workflow to your use case? Engage with fellow creators in the n8n Discord or community forum:
   
   → Discord: https://discord.com/invite/XPKeKXeB7d  
   → Forum: https://community.n8n.io/
   
   Let automation meet conversation—you’ll be amazed what your chatbot can do.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.